Does the relation between the control of attention and second language proficiency generalize from India to Canada?
Bibliographic record
Abstract
Over the last decades, the extralinguistic benefits of bilingualism have been intensively debated. The current study was aimed at clarifying whether bilingualism speeds attentional disengagement. Reflecting faster disengagement, Mishra, Hilchey, Singh, and Klein (2012) observed an earlier onset of inhibition of return (IOR) for high than for low-proficient bilinguals. In contrast, Hernandez, Costa, Fuentes, Vivas, and Sebastian-Galles (2010) failed to find any difference between bilinguals and monolinguals. We investigated the source of this discrepancy, while improving methodology by using a large sample composed of 100 Canadians, objective assessments of second language skills (Nelson-Denny Reading test), and controlling for nonverbal intelligence, age, sex, and video-gaming. Results were analyzed with self-report and objective measures of second language proficiency as well as dichotomous and continuous measures. Compared to less proficient bilinguals, highly proficient bilinguals tended to respond faster overall, hinting at an executive processing advantage. However, contrary to Mishra et al.'s findings, bilingual proficiency did not affect either the onset of IOR or magnitude of IOR. (PsycINFO Database Record
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".